System and Method for Assortment Planning with Interactive Similarity and Transferable Demand Visualization

ABSTRACT

A system and method are disclosed for interactive product assortment planning and visualization by receiving product attribute values for items of a product assortment is disclosed. Embodiments include displaying icons on an interactive visualization, connecting the icons with transferable demand links, identifying items to be removed from a product assortment, and transporting items among one or more supply chain entities.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/240,663, filed on Apr. 26, 2021, entitled “System and Method forAssortment Planning with Interactive Similarity and Transferable DemandVisualization,” which is a continuation of U.S. patent application Ser.No. 15/802,088, filed on Nov. 2, 2017, entitled “System and Method forAssortment Planning with Interactive Similarity and Transferable DemandVisualization,” now U.S. Pat. No. 10,997,615, which claims the benefitunder 35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/419,037,filed Nov. 8, 2016, and entitled “System and Method for Similarity andTransferable Demand Interactive Visualization.” U.S. patent applicationSer. No. 17/240,663, U.S. Pat. No. 10,997,615, and U.S. ProvisionalApplication No. 62/419,037 are assigned to the assignee of the presentapplication.

TECHNICAL FIELD

The present disclosure relates generally to assortment planning andspecifically to a system and method for visualization for productsimilarity and transferable demand of product assortments.

BACKGROUND

Determining whether to add or remove items from a product assortment isaided by a transferable demand analysis. A transferable demand analysispredicts how much demand transfers between products in assortments. Theassortment planner may then calculate, using the transferable demand,whether sales will increase or decrease when an item is added to orremoved from an assortment. Using this, and other criteria, theassortment planner may determine what products should be included in newproduct assortments. The process to analyze demand transferability,however, is generally difficult since one needs to easily visualize thesimilarity that exists between products in the assortments, which is notprovided by existing solutions. The inability to easily visualize andanalyze product similarity and demand transferability for productassortments is undesirable.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention may be derived byreferring to the detailed description when considered in connection withthe following illustrative figures. In the figures, like referencenumbers refer to like elements or acts throughout the figures.

FIG. 1 illustrates an exemplary supply chain network according to afirst embodiment;

FIG. 2 illustrates the assortment planner of FIG. 1 in greater detail inaccordance with an embodiment;

FIG. 3 illustrates an exemplary method of generating an interactivetwo-dimensional assortment visualization, according to an embodiment;

FIG. 4 illustrates an exemplary similarity matrix, according to anembodiment;

FIG. 5 illustrates an exemplary two-dimensional assortmentvisualization, according to an embodiment;

FIG. 6 illustrates an exemplary view of two-dimensional assortmentvisualization of FIG. 5 , according to a further embodiment;

FIG. 7 illustrates an exemplary interactive two-dimensional assortmentvisualization according to an embodiment;

FIG. 8 illustrates an exemplary view of the interactive two-dimensionalassortment visualization of FIG. 7 , according to a further embodiment;

FIG. 9 illustrates an exemplary method of modifying a product assortmentusing interactive two-dimensional visualization, according to anembodiment; and

FIG. 10 illustrates an exemplary view of the interactive two-dimensionalassortment visualization of FIG. 7 , according to an embodiment.

DETAILED DESCRIPTION

Aspects and applications of the invention presented herein are describedbelow in the drawings and detailed description of the invention. Unlessspecifically noted, it is intended that the words and phrases in thespecification and the claims be given their plain, ordinary, andaccustomed meaning to those of ordinary skill in the applicable arts.

In the following description, and for the purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the various aspects of the invention. It will beunderstood, however, by those skilled in the relevant arts, that thepresent invention may be practiced without these specific details. Inother instances, known structures and devices are shown or discussedmore generally in order to avoid obscuring the invention. In many cases,a description of the operation is sufficient to enable one to implementthe various forms of the invention, particularly when the operation isto be implemented in software. It should be noted that there are manydifferent and alternative configurations, devices and technologies towhich the disclosed inventions may be applied. The full scope of theinventions is not limited to the examples that are described below.

As described more fully below, aspects of the following disclosurerelate to interactive visualization of similarity and transferabledemand associated with product assortment planning in retailers. In theretail industry, a product, such as clothing, may be defined by one ormore attributes, including, for example, color, material, design,pattern, length or the like. Each attribute may have a differentattribute value. These attribute values include, for example, red, blue,green (for color), silk, cotton, polyester (for material), fashion,basic, classic (for design), striped, floral, plaid (for pattern), long,short, high, (for length), and other like attributes and attributevalues, according to particular needs. These attribute values alsodetermine, at least in part, customer preferences, individually and ascustomer segments defined by similar customer shopping behavior,preferences for purchasing items with particular attribute values, or acombination of both. One goal of assortment planning is to choose anassortment of products to sell during a planning period that matchespredicted customer preferences during the planning period. As anexample, this may include, for a clothing retailer, choosing anassortment of different clothing products that will match the style,colors, season, and trends predicted to be favored by customers during aplanning period. Although assortment planning, attributes and attributevalues are described in connection with a clothing retailer, embodimentscontemplate assortment planning with attributes and attribute values ofany retailer, including, for example, fashion retailers, groceryretailers, parts retailers, and the like.

Summarizing assortment data may be done using a tabular format, withrows or columns corresponding to products and attribute values. Usingtabular data for assortment planning is problematic because tabular datadoes not show the overall similarity of the assortment or how productsaffect the sales of other products (i.e. the transferability of demand),which gives insight to how the exclusivity percentage for one or moreproducts in an assortment is determined. For example, if a retailer hasa little black dress that is a top seller but only carries similarlittle black dresses in all of their stores, the retailer will lose alot of sales because the products (the black dresses) are so similarthat consumers will not purchase very many of them. Additionally, withdata displayed in a tabular format, insights and intuition are dulled byexcess information. The tabular format makes it difficult to assess thediversity of an assortment this way, and to determine whether a productassortment is over-assorted, under-assorted, missing in exclusiveproducts, and the like.

Embodiments of the following disclosure detail systems and methods ofinteractively visualizing and selecting assortment products based, atleast in part, on similarity and transferable demand. According to afirst approach of similarity and transferable demand interactivevisualization, embodiments disclose a similarity method that computesthe similarity between products and reduces the dimensionality in a setof coordinates based on the attributes that the products share incommon. This approach uses a similarity function to construct asimilarity matrix of the products in the assortment representing howsimilar each product is compared with another product. The similaritymatrix is used as the basis for computing the coordinates for eachproduct in an n-dimensional space. This approach aims at visuallyrepresenting the overall inter-product similarities in an assortment bygenerating an interactive two-dimensional assortment visualization.

According to a second approach of similarity and transferable demandinteractive visualization, embodiments disclose a visualization methodthat determines products which share a similar status in terms of theirperformance as well as in terms of their exclusivity. This allowsassortment planning decisions to account for both individual productperformance and influences a product has on other products'performances.

FIG. 1 illustrates exemplary supply chain network 100 according to afirst embodiment. Supply chain network 100 comprises assortment planner110, inventory system 120, one or more imagers 130, transportationnetwork 140, one or more supply chain entities 150 150, computer 160,network 170, and communication links 180-190. Although a singleassortment planner 110, a single inventory system 120, one or moreimagers 130, a single transportation network 140, one or more supplychain entities 150, a single computer 160, a single network 170, and oneor more communication links 180-190 are shown and described, embodimentscontemplate any number of assortment planners, inventory systems,imagers, transportation systems, supply chain entities, computers,networks, or communication links, according to particular needs.

In one embodiment, assortment planner 110 comprises server 112 anddatabase 114. As explained in more detail below, assortment planner 110computes product similarity, generates one or more assortment planningvisualizations of product similarity and transferable demand, determinesproduct assortments, and places orders for products in the assortment.

Inventory system 120 comprises server 122 and database 124. Server 122of inventory system 120 is configured to receive and transmit item data,including item identifiers, pricing data, attribute data, inventorylevels, and other like data about one or more items at one or morelocations in supply chain network 100. Server 122 stores and retrievesitem data from database 124 or one or more locations in supply chainnetwork 100.

One or more imagers 130 comprise one or more electronic devices thatreceive imaging information from one or more sensors 136 or from one ormore databases in supply chain network 100. According to embodiments,one or more imagers 130 comprise one or more processors 132, memory 134,and one or more sensors 136 and may include any suitable input device,output device, fixed or removable computer-readable storage media, orthe like. According to embodiments, one or more imagers 130 identifyitems near the one or more sensors 136 and generate a mapping of theitem in supply chain network 100. As explained in more detail below,inventory system 120 and transportation network 140 use the mapping ofan item to locate the item in supply chain network 100. The location ofthe item is then used to coordinate the storage and transportation ofitems in supply chain network 100 to implement one or more productassortments generated by assortment planner 110.

One or more imagers 130 may comprise a mobile handheld device such as,for example, a smartphone, a tablet computer, a wireless device, or thelike. In addition, or as an alternative, one or more imagers 130comprise one or more networked electronic devices configured to transmititem identity information to one or more databases as an item passes byor is scanned by one or more imagers 130. This may include, for example,a stationary scanner located at one or more supply chain entities 150that identifies items as the items pass near the scanner, such as, forexample, a point of sale system at one or more retailers 158 thatrecords transaction data and associates the transaction data withproduct data, including, for example, associating customer identitydata, store data, market data, time data, price data, discount data, andthe like with product identity data and attribute data. One or moresensors 136 of one or more imagers 130 may comprise an imaging sensor,such as, a camera, scanner, electronic eye, photodiode, charged coupleddevice (CCD), or any other electronic or manual sensor that detectsimages of objects. In addition, or as an alternative, one or moresensors 136 may comprise a radio receiver and/or transmitter configuredto read an electronic tag, such as, for example, an RFID tag.

Transportation network 140 comprises server 142 and database 144.According to embodiments, transportation network 140 directs one or moretransportation vehicles 146 to ship one or more items between one ormore supply chain entities 150, based, at least in part, on the productassortment generated by assortment planner 110. Transportation vehicles146 comprise, for example, any number of trucks, cars, vans, boats,airplanes, unmanned aerial vehicles (UAVs), cranes, robotic machinery,or the like. Transportation vehicles 146 may comprise radio, satellite,or other communication that communicates location information (such as,for example, geographic coordinates, distance from a location, globalpositioning satellite (GPS) information, or the like) with assortmentplanner 110, inventory system 120, one or more imagers 130,transportation network 140, and/or one or more supply chain entities 150to identify the location of the transportation vehicle 146 and thelocation of any inventory or shipment located on the transportationvehicle 146. In addition to the product assortment, the number of itemsshipped by transportation vehicles 146 in transportation network 140 mayalso be based, at least in part, on an inventory policy, target servicelevels, the number of items currently in stock at one or more supplychain entities 150, the number of items currently in transit intransportation network 140, forecasted demand, a supply chaindisruption, and the like.

As shown in FIG. 1 , supply chain network 100 operates on one or morecomputers 160 that are integral to or separate from the hardware and/orsoftware that support assortment planner 110, inventory system 120, oneor more imagers 130, transportation network 140, and one or more supplychain entities 150. Computers 160 may include any suitable input device162, such as a keypad, mouse, touch screen, microphone, or other deviceto input information. Output device 164 may convey informationassociated with the operation of supply chain network, including digitalor analog data, visual information, or audio information. Computer 160may include fixed or removable computer-readable storage media,including a non-transitory computer readable medium, magnetic computerdisks, flash drives, CD-ROM, in-memory device or other suitable media toreceive output from and provide input to supply chain network 100.Computer 160 may include one or more processors 166 and associatedmemory to execute instructions and manipulate information according tothe operation of supply chain network 100 and any of the methodsdescribed herein. In addition, or as an alternative, embodimentscontemplate executing the instructions on computer 160 that causecomputer 160 to perform functions of the method. Further examples mayalso include articles of manufacture including tangiblecomputer-readable media that have computer-readable instructions encodedthereon, and the instructions may comprise instructions to performfunctions of the methods described herein. According to someembodiments, the functions and methods described in connection with oneor more imagers 130 may be emulated by one or more modules configured toperform the functions and methods as described.

In addition, and as discussed herein, supply chain network 100 maycomprise a cloud-based computing system having processing and storagedevices at one or more locations, local to, or remote from assortmentplanner 110, inventory system 120, one or more imagers 130,transportation network 140, and one or more supply chain entities 150.In addition, each of one or more computers 160 may be a work station,personal computer (PC), network computer, notebook computer, tablet,personal digital assistant (PDA), cell phone, telephone, smartphone,wireless data port, augmented or virtual reality headset, or any othersuitable computing device. In an embodiment, one or more users may beassociated with assortment planner 110, inventory system 120, one ormore imagers 130, transportation network 140, and one or more supplychain entities 150. These one or more users may include, for example, a“buyer” or a “planner” handling retail product assortment, managing theinventory of items, imaging items, managing storage and shipment ofitems, and/or one or more related tasks within supply chain network 100.In addition, or as an alternative, these one or more users within supplychain network 100 may include, for example, one or more computers 160programmed to autonomously handle, among other things, evaluation ofvarious levels of retail process management, determining an assortmentplan, forecasting demand, controlling manufacturing equipment, andadjusting various levels of manufacturing and inventory levels atvarious stocking points and distribution centers, and/or one or morerelated tasks within supply chain network 100.

Supply chain entities 150 represent one or more supply chain networks100, including one or more enterprises, such as, for example networks ofone or more suppliers 152, manufacturers 154, distribution centers 156,retailers 158 (including brick and mortar and online stores), customers,and/or the like. Suppliers 152 may be any suitable entity that offers tosell or otherwise provides one or more items (i.e., materials,components, or products) to one or more manufacturers 154. Suppliers 152may comprise automated distribution systems 153 that automaticallytransport products to one or more manufacturers 154 based, at least inpart, on a product assortment determined by assortment planner 110and/or one or more other factors described herein. In addition, or as analternative, each of the one or more items may be represented in supplychain network 100 by an identifier, including, for example,Stock-Keeping Unit (SKU), Universal Product Code (UPC), serial number,barcode, tag, RFID, or any other device that encodes identifyinginformation. As discussed above, one or more imagers 130 may generate amapping of one or more items in supply chain network 100 by scanning anidentifier associated with an item or associating the image of an itemwith an identifier stored in a database.

Manufacturers 154 may be any suitable entity that manufactures at leastone product. Manufacturers 154 may use one or more items during themanufacturing process to produce any manufactured, fabricated,assembled, or otherwise processed item, material, component, good orproduct. In one embodiment, a product represents an item ready to besupplied to, for example, one or more supply chain entities 150 insupply chain network 100, such as retailers 152, an item that needsfurther processing, or any other item. Manufacturers 154 may, forexample, produce and sell a product to suppliers 152, othermanufacturers 154, distribution centers 156, retailers 158, a customer,or any other suitable person or entity. Manufacturers 154 may compriseautomated robotic production machinery 155 that produce products based,at least in part, on a product assortment determined by assortmentplanner 110 and/or one or more other factors described herein.

Distribution centers 156 may be any suitable entity that offers to storeor otherwise distribute at least one product to one or more retailers158 and/or customers. Distribution centers 156 may, for example, receivea product from a first one or more supply chain entities 150 in supplychain network 100 and store and transport the product for a second oneor more supply chain entities 150. Distribution centers 156 may compriseautomated warehousing systems 157 that automatically remove productsfrom and place products into inventory based, at least in part, on aproduct assortment determined by assortment planner 110 and/or one ormore other factors described herein.

Retailers 158 may be any suitable entity that obtains one or moreproducts to sell to one or more customers. Retailers 158 may compriseany online or brick and mortar store, including stores with shelvingsystems 159. Shelving systems 159 may comprise, for example, variousracks, fixtures, brackets, notches, grooves, slots, or other attachmentdevices for fixing shelves in various configurations. Theseconfigurations may comprise shelving systems 159 with adjustablelengths, heights, and other arrangements, which may be adjusted by anemployee of retailers based on computer-generated instructions orautomatically by machinery to place products in a desired location inretailers.

Although one or more supply chain entities 150 are shown and describedas separate and distinct entities, the same entity may simultaneouslyact as any one of supply chain entities 150. For example, one or moresupply chain entities 150 acting as a manufacturer 154 can produce aproduct, and the same entity can act as supplier 152 to supply an itemto itself or another of one or more supply chain entity 150. Althoughone example of supply chain network 100 is shown and described,embodiments contemplate any configuration of supply chain network 100,without departing from the scope described herein.

In one embodiment, assortment planner 110 may be coupled with network170 using communications link 180, which may be any wireline, wireless,or other link suitable to support data communications between assortmentplanner 110 and network 170 during operation of supply chain network100. Inventory system 120 may be coupled with network 170 usingcommunications link 182, which may be any wireline, wireless, or otherlink suitable to support data communications between inventory system120 and network 170 during operation of supply chain network 100. One ormore imagers 130 are coupled with network 170 using communications link184, which may be any wireline, wireless, or other link suitable tosupport data communications between one or more imagers 130 and network170 during operation of supply chain network 100. Transportation network140 may be coupled with network 170 using communications link 186, whichmay be any wireline, wireless, or other link suitable to support datacommunications between transportation network 140 and network 170 duringoperation of supply chain network 100. One or more supply chain entities150 may be coupled with network 170 using communications link 188, whichmay be any wireline, wireless, or other link suitable to support datacommunications between one or more supply chain entities 150 and network170 during operation of supply chain network 100. Computer 160 may becoupled with network 170 using communications link 190, which may be anywireline, wireless, or other link suitable to support datacommunications between computer 160 and network 170 during operation ofsupply chain network 100.

Although communication links 180-190 are shown as generally couplingassortment planner 110, inventory system 120, one or more imagers 130,transportation network 140, one or more supply chain entities 150, andcomputer 160 to network 170, each of assortment planner 110, inventorysystem 120, one or more imagers 130, transportation network 140, one ormore supply chain entities 150, and computer 160 may communicatedirectly with each other, according to particular needs.

In another embodiment, network 170 includes the Internet and anyappropriate local area networks (LANs), metropolitan area networks(MANs), or wide area networks (WANs) coupling assortment planner 110,inventory system 120, one or more imagers 130, transportation network140, one or more supply chain entities 150, and computer 160. Forexample, data may be maintained by locally or externally of assortmentplanner 110, inventory system 120, one or more imagers 130,transportation network 140, one or more supply chain entities 150, andcomputer 160 and made available to one or more associated users ofassortment planner 110, inventory system 120, one or more imagers 130,transportation network 140, one or more supply chain entities 150, andcomputer 160 using network 170 or in any other appropriate manner. Forexample, data may be maintained in a cloud database at one or morelocations external to assortment planner 110, inventory system 120, oneor more imagers 130, transportation network 140, one or more supplychain entities 150, and computer 160 and made available to one or moreassociated users of assortment planner 110, inventory system 120, one ormore imagers 130, transportation network 140, one or more supply chainentities 150, and computer 160 using the cloud or in any otherappropriate manner. Those skilled in the art will recognize that thecomplete structure and operation of network 170 and other componentswithin supply chain network 100 are not depicted or described.Embodiments may be employed in conjunction with known communicationsnetworks and other components.

In accordance with the principles of embodiments described herein,assortment planner 110 performs an interactive visualization analysis todetermine an assortment of products based, at least in part, on theattribute values of the products in the assortment. For example, when aproduct is removed from an assortment, a set of attribute values isremoved and the shares of the products can be distributed to otherproducts by considering and ranking the products with a similar set ofattribute values. Likewise, when an exclusive product is removed fromthe assortment, a set of attribute values is removed and the shares ofthe products will not be distributed to other products because no othersubstitutable products are available. In addition, or as an alternative,assortment planner 110 monitors the inventory of the one or moreproducts and adjusts the inventory, product assortment, removal oraddition of items, new collection assortment of the retailer and/orsupply chain entities based, at least in part, on the interactivevisualization analysis.

According to embodiments, assortment planner 110 generates an assortmentplan and determines an assortment of one or more products at a retailerand/or supply chain entity based, at least in part, on an interactivevisualization analysis. Assortment planner 110 further generates productcombinations, places product orders at various manufacturers 154 and/ordistribution centers 156, initiates manufacturing of the products anddetermines products to be carried at various retailers 158.Additionally, or in the alternative, assortment planner 110 may generatea buy quantity for the inventory of one or more supply chain entities150 in supply chain network 100. Furthermore, assortment planner 110,inventory system 120, and/or transportation network 140 may instructautomated machinery (i.e., robotic warehouse systems, robotic inventorysystems, automated guided vehicles, mobile racking units, automatedrobotic production machinery, robotic devices and the like) to adjustproduct mix ratios, inventory levels at various stocking points,production of products of manufacturing equipment, proportional oralternative sourcing of one or more supply chain entities 150, and theconfiguration and quantity of packaging and shipping of products based,at least in part, on one or more generated assortment plans, determinedassortments, inventory policies, and/or current inventory or productionlevels. For example, according to embodiments, assortment planner 110determines a rate of sale and/or a purchase quantity for one or moreproducts in one or more assortments, which is may be used, incombination with inventory policies or target service levels, to signifywhen the inventory quantity of an item reaches a particular level, theitem may resupplied. Therefore, when the inventory of an item falls to acertain level, assortment planner 110 may initiate one or more processesthat then automatically adjusts product mix ratios, inventory levels,production of products of manufacturing equipment, and proportional oralternative sourcing of one or more supply chain entities 150 until theinventory is resupplied to a target level.

For example, the methods described herein may include computersreceiving product data from automated machinery having at least onesensor 136 and the product data corresponding to an item detected by theautomated machinery. The received product data may include an image ofthe item, an identifier, as described above, and/or other product dataassociated with the item (dimensions, texture, estimated weight, and anyother like data). The method may further include computers looking upthe received product data in a database system associated withassortment planner 110, inventory system 120, and/or transportationnetwork 140 to identify the item corresponding to the product datareceived from the automated machinery.

Computers 160 may also receive, from the automated machinery, a currentlocation of the identified item. Based on the identification of theitem, computers 160 may also identify (or alternatively generate) afirst mapping in the database system, where the first mapping isassociated with the current location of the item. Computers 160 may alsoidentify a second mapping in the database system, where the secondmapping is associated with a past location of the identified item.Computers 160 may also compare the first mapping and the second mappingto determine if the current location of the identified item in the firstmapping is different than the past location of the identified item inthe second mapping. Computers 160 may then send instructions to theautomated machinery based, as least in part, on one or more differencesbetween the first mapping and the second mapping such as, for example,to locate item to add to or remove from an inventory of or shipment forone or more supply chain entities 150. In addition, or as analternative, assortment planner 110 monitors the supply chainconstraints of one or more items at one or more supply chain entities150 and adjusts the orders and/or inventory of supply chain entities 150based on the supply chain constraints.

FIG. 2 illustrates assortment planner 110 of FIG. 1 in greater detail inaccordance with an embodiment. As discussed above, assortment planner110 comprises server 112 and database 114. Although assortment planner110 is shown as comprising a single server 112 and a single database114, embodiments contemplate any suitable number of servers or databasesinternal to or externally coupled with assortment planner 110, accordingto particular needs. According to some embodiments, assortment planner110 may be located internal to one or more retailers 158 of one or moresupply chain entities 150. In other embodiments, assortment planner 110may be located external to one or more retailers 158 of one or moresupply chain entities 150 and may be located in for example, a corporateretailer of the one or more retailers 158, according to particularneeds.

Server 112 of assortment planner 110 may comprise similarity module 202,visualization module 204, product substitution module 206, and productassortment generator 208. Although server 112 is shown and described ascomprising a single similarity module 202, visualization module 204,product substitution module 206, and product assortment generator 208,embodiments contemplate any suitable number or combination of theselocated at one or more locations, local to, or remote from assortmentplanner 110, such as on multiple servers or computers at any location insupply chain network 100.

Similarity module 202 computes the similarity between each pair ofproducts in a product assortment. According to embodiments, similaritymodule 202 generates similarity matrix 400 (FIG. 4 ), determinescoordinates to represent a product in an assortment in an assortmentvisualization, and summarizes the product similarity, which is used asthe basis for n-dimensional mapping. Visualization module 204 generatesan assortment visualization which displays products in terms ofindividual sales performance and uniqueness. According to embodiments,visualization module 204 graphically displays product performanceinteractions which help users more easily select products for a productassortment.

Product substitution module 206 generates a visualization oftransferable demand that represents how the demand transfers between theproducts in an assortment. According to embodiments, productsubstitution module 206 displays a graphical user interface (GUI) thatallows a user to navigate products in a product assortment and visualizethe transferable demand among them. Product assortment generator 208 ofassortment planner 110 generates product assortment by indicating theproducts that will be included or excluded in a product assortment for aparticular planning period. The product assortment may be based on, forexample, data regarding sales, profitability, transferable demand,similarity, or the like for any one or more products or assortments.Assortment purchase planner 210 may calculate a purchase quantity ofitems in an assortment and place an order based on, for example, a newproduct assortment.

Database 114 of assortment planner 110 may comprise one or moredatabases or other data storage arrangement at one or more locations,local to, or remote from, server 112. Database 114 comprises, forexample, historical data 220, product data 222, assortment data 224,inventory data 226, demand forecasts 228, inventory policies 230, andstore data 232. Although, database 114 is shown and described ascomprising historical data 220, product data 222, assortment data 224,inventory data 226, demand forecasts 228, inventory policies 230, andstore data 232, embodiments contemplate any suitable number orcombination of these, located at one or more locations, local to, orremote from, assortment planner 110 according to particular needs.

Historical data 220 may comprise data relating to the one or moreproducts, including, for example, sales data, geographical regions,store locations, inventory data, time periods, seasonality, or othertypes of dimensions. In addition, the historical data may be representedby any suitable combination of values and dimensions, aggregated orun-aggregated, such as, for example, transaction data, sales per week,sales per week per location, sales per day, sales per day per season, orthe like. The transaction data may comprise data that records salestransactions and related data, including, for example, a transactionidentification, time and date stamp, store identification, productidentification, actual cost, selling price, sales quantity, customeridentification, and or the like.

Product data 222 of database 114 may comprise one or more datastructures comprising products identified by, for example, a productidentification (such as a Stock Keeping Unit (SKU), Universal ProductCode (UPC) or the like) and one or more attributes and attribute valuesassociated with the product ID, which may be stored as attribute data.Product data 222 may comprise any attributes or attribute values of oneor more products organized according to any suitable database structure,and sorted by, for example, attribute, attribute value, productidentification, or any suitable categorization or dimension. Attributesof one or more items may be, for example, any categorical characteristicor quality of an item, and an attribute value may be a specific value oridentity for the one or more items according to the categoricalcharacteristic or quality.

As an example only and not by way of limitation, a product, such as forexample, a shirt, may comprise the attributes of gender, season, articleof clothing, color, sleeve-length, price segment, pattern, or the like.Attribute values for these attributes may comprise, for example, male orfemale, for gender; spring, summer, fall, winter, for season; top,blouse, shirt, bottom, pants, shorts, skirt, or the like, for article ofclothing; red, blue, green, or the like, for color; long, short, medium,or the like, for sleeve-length; good, better, best, for price segment;stripe, checked, plain, or the like, for pattern. Although particularproducts comprising particular attributes and attribute values aredescribed herein, embodiments contemplate any item, attribute orattribute value, accordingly to particular needs.

Assortment data 224 of database 114 may comprise the identity ofproducts selected for an assortment and the attribute values associatedwith those products.

Inventory data 226 of database 114 may comprise any data relating tocurrent or projected inventory quantities or states, order rules, or thelike. For example, inventory data 226 may comprise the current level ofinventory for each item at one or more stocking points across supplychain network 100. In addition, inventory data 226 may comprise orderrules that describe one or more rules or limits on setting an inventorypolicy, including, but not limited to, a minimum order quantity, amaximum order quantity, a discount, and a step-size order quantity, andbatch quantity rules. According to some embodiments, inventory data 226may comprise explanatory variables that describe the data relating tospecific past, current, or future indicators and the data of promotions,seasonality, special events (such as sporting events), weather, and thelike. According to some embodiments, assortment planner 110 accesses andstores inventory data 226 in database 114, which may be used byassortment planner 110 to place orders, set inventory levels at one ormore stocking points, initiate manufacturing of one or more components,or the like. In addition, or as an alternative, inventory data 226 maybe updated by receiving current item quantities, mappings, or locationsfrom inventory system 120, one or more imagers 130, and/ortransportation system 140.

Demand forecasts 228 of database 114 may indicate future expected demandbased on, for example, any data relating to past sales, past demand,purchase data, promotions, events, or the like of one or more supplychain entities 150. Demand forecasts 228 may cover a time interval suchas, for example, by the minute, hour, daily, weekly, monthly, quarterly,yearly, or any suitable time interval, including substantially in realtime.

Inventory policies 230 of database 114 may comprise any suitableinventory policy describing the reorder point and target quantity, orother inventory policy parameters that set rules for assortment planner110 to manage and reorder inventory. Inventory policies 230 may be basedon target service level, demand, cost, fill rate, or the like. Accordingto embodiment, inventory policies 230 comprise target service levelsthat ensure that a service level of one or more supply chain entities150 is met with a certain probability. For example, one or more supplychain entities 150 may set a service level at 95%, meaning Supply chainentities 150 will set the desired inventory stock level at a level thatmeets demand 95% of the time. Although, a particular service leveltarget and percentage is described; embodiments contemplate any servicetarget or level, for example, a service level of approximately 99%through 90%, a 75% service level, or any suitable service level,according to particular needs. Other types of service levels associatedwith inventory quantity or order quantity may comprise, but are notlimited to, a maximum expected backlog and a fulfillment level. Once theservice level is set, assortment planner 110 may determine areplenishment order according to one or more replenishment rules, which,among other things, indicates to one or more supply chain entities 150to determine or receive inventory to replace the depleted inventory.

Store data 232 of database 114 may comprise data describing the storesof one or more retailers 154 and related store information. Store data232 may comprise, for example, a store ID, store description, storelocation details, store location climate, store type, store openingdate, lifestyle, store area (expressed in, for example, square feet,square meters, or other suitable measurement), latitude, longitude, andother like store data.

FIG. 3 illustrates an exemplary method of generating an interactivetwo-dimensional assortment visualization based on product similarity,according to an embodiment. Although actions of the method are describedin a particular order, embodiments contemplate actions performed in anysuitable order or combination according to particular needs.

At action 302, assortment planner receives product data. According toembodiments, assortment planner 110 receives product data 222 from oneor more supply chain entities 150 in supply chain network 100 for one ormore products and stores the data in the database of assortment planner110. At action 304, assortment planner generates a similarity matrix 400(FIG. 4 ) from product data. As explained in more detail below,similarity module 202 generates similarity matrix 400 comprising thesimilarity of product attribute values between various products.

At action 306, assortment planner 110 performs a dimensionalityreduction method on the similarity matrix 400. As explained in moredetail below, similarity module 202 reduces similarity matrix 400 froman n×n matrix to an n×2 or n×3 matrix. At action 308, assortment planner110 generates n-dimensional coordinates based, at least in part, on thereduced matrix.

At action 310, assortment planner 110 generates a two-dimensionalvisualization of product similarity. As explained in more detail below,similarity module 202 displays a two-dimensional visualization, whichmay also comprise a user interface for selecting products in a productassortment. Although the visualization is described as a two-dimensionalvisualization, embodiments contemplate a three-dimensionalvisualization, such as, for example, displayed on a virtual or augmentedreality headset or a display (such as, for example, a monitor,projector, or the like) capable of displaying three-dimensional images.

To further describe method 300 of generating an interactiven-dimensional assortment visualization based on product similarity, anexample is now given. In the following example, and after receivingproduct data, similarity module 202 generates a similarity matrix bycomputing the similarity between different products. That is, thesimilarity module uses a similarity function to construct a squaresymmetric similarity matrix of all of the products in the assortmentbased on the attribute values of the products and which provides datarepresenting how similar each product is compared with another product.The square symmetric similarity matrix summarizes the similarity betweenevery possible pair of products and as discussed below in more detail,is used as the basis for n-dimensional mapping.

FIG. 4 illustrates an exemplary similarity matrix 400, according to anembodiment. According to embodiments, similarity matrix 400 comprises anx-axis that lists a set of products plotted against a y-axis, comprisingan identical set of products. Each intersection of a product from thex-axis with a product on the y-axis indicates the similarity betweenthose two products, as defined by a similarity function. According to anembodiment, the similarity function quantifies the similarity betweenproducts, based on a comparison of their respective attribute values.

According to a first embodiment, the similarity function comprises thepercentage of attributes that share the same attribute values betweentwo products. As a simple example only and not by way of limitation, ifa first product is a blue dress and a second product is a red dress andthe products are assumed to have only two attributes (type, and color),then the similarity calculated by this similarity function is 50%,because both products have the same value for the attribute (dress), butboth products have a different value for the “color” attribute (red vs.blue).

As another example only and not by way of limitation, similarity matrix400 indicates that the similarity between a first product (Product 0)and a second product (Product 1) is 76%, which indicates a differencebetween some attribute values between the two products. Because eachproduct is represented on both the x-axis and the y-axis, the similarityof each product is also compared with itself. For example, thesimilarity between a first product (Product 0) on the x-axis and itself(Product 0) on the y-axis is 100%, which would be expected because thisindicates the similarity between a product and itself. Other percentagesof similarity between the remaining products are equal to thepercentages indicated in similarity matrix 400.

According to further embodiments, the similarity function may comprisethe number of identical attribute values between two products, weightedby the importance of each attribute. According to another embodiment,the similarity function comprises a cosine distance.

Once the similarity between the products are quantified, similaritymodule 202 maps similarity matrix 400 onto an n-dimensionalvisualization by reducing the dimensionality in the set of vectors usinga dimensionality reduction method to visualize the product assortmentsimilarity. In addition, or as an alternative, similarity module 202uses the dimensionality reduction method to produce the visualizationfrom a set of products in an assortment that has similarity i.e., a setof products that share or do not share some attribute values.

FIG. 5 illustrates an exemplary two-dimensional visualization 500,according to an embodiment. Two-dimensional visualization 500 comprisesvarious bubbles 502 that each represent a product in an assortment. Asdescribed in more detail below, the location on two-dimensionalvisualization 500 where bubbles 502 are displayed indicate thesimilarity of the products represented by bubbles 502. Two-dimensionalvisualization 500 comprises an x-axis and a y-axis. The (x,y) coordinateof a given bubble 502 relative to any other bubble 502 indicates thesimilarity or exclusivity of the products represented by bubbles 502 inthe product assortment. Products which have mostly similar attributevalues are represented by bubbles 502 that are positioned closelytogether. Similar products may, for example, be represented by patternsof bubbles 502 clustered closely together, such as, for example, theclosely clustered products in area 504. Products which have less similarattribute values are represented by bubbles 502 that are positionedfurther from each other. Less similar products may, for example, berepresented by patterns of bubbles 502 clustered further apart, such as,for example, the distantly clustered products in area 506. The moredistantly any of bubbles 502 are positioned from each other ontwo-dimensional visualization 500, the more different the attributevalues of the products represented by bubbles 502 are. Although aparticular two-dimensional visualization 500 is shown and described,embodiments contemplate a three-dimensional visualization,four-dimensional visualization, or any n-dimensional visualization,according to particular needs.

As discussed above, similarity module 202 determines the (x, y)coordinates from similarity matrix 400 computed with the similarityfunction between all pairs of products. This is obtained by theconsistent description of products by means of distinct attributes andrespective possible attribute values. In one embodiment, similaritymodule 202 computes the similarity between two products by averaging theresult of an attribute-wise comparison. Transforming the similaritymatrix into (x, y) coordinates corresponds to performing dimensionalityreduction on a set of vectors whose components share the same meaning.

In one embodiment, similarity module 202 uses a principal componentanalysis (PCA) algorithm as the dimensionality reduction method todetermine the product assortment similarity. This method reduces thedimensionality of similarity matrix 400 while preserving as muchvariance as possible in the data. In addition, similarity module 202transforms the square n×n matrix into a n×2 matrix, using the tworemaining components as (x, y) coordinates. This method provides forclustering similar products together, as well as, spreading dissimilarproducts apart.

FIG. 6 illustrates an exemplary view of two-dimensional visualization500 of FIG. 5 , according to a further embodiment. As shown above,two-dimensional visualization 500 comprises a mouse-over of a bubble,representing a particular product illustrating a linking of otherproducts that share similar attribute values. In addition, and asdiscussed in more detail below, additional information can be added tothe dimensional visualization, such as, for example size and color ofthe bubbles representing products, which also may be used to representpast sales, exclusivity of a product, and the like. For example, asdescribed below, user selection of a product generates a popup box withadditional information or connectors which indicate its most similarneighbors. Additionally, or in the alternative, a size of the projectedbubbles 502 may graphically indicate uniqueness, and a color of bubbles502 may be used to indicate past sales performance, as described in moredetail below.

According to embodiments, and as will be explained in more detail below,visualization module 204 of assortment planner 110 displays productswhich share a similar status in terms of their performance as well as interms of their exclusivity.

FIG. 7 illustrates an exemplary interactive two-dimensionalvisualization 700 according to an embodiment. Visualization module 204of assortment planner 110 provides an interactive two-dimensionalvisualization 700 that represents various assortment scenarios. Asdiscussed above, each bubble 502 in two-dimensional visualization 500represents a product in an assortment. However, according toembodiments, interactive two-dimensional visualization 700 comprisessize-variable and color-coded bubbles 702-708. Here, a size of bubbles702-708 represents the uniqueness of the product based on the amount ofdemand that is exclusive to the product. For example, a larger bubblemay represent a more exclusive product, and a smaller bubble mayrepresent a less exclusive product. Bubbles 702-708 may comprise one ormore intermediate sizes that represent exclusivity intermediate betweenthe larger bubbles and the smaller bubbles. In other words, according tosome embodiments, the larger a bubble is displayed on interactivetwo-dimensional visualization 700, the more exclusive the productrepresented by the bubble is, and the smaller a bubble is displayed oninteractive two-dimensional visualization 700, the less exclusive theproduct represented by the bubble is.

In addition, a color of bubbles 702-708 may represent the performance orpredictive performance of the product represented by the bubble 702-708.Performance or predictive performance may be based, at least in part,on, for example, historical or predicted sales performance. In thismanner, bubbles 702-708 visually represent the exclusivity of theproduct, while simultaneously visually representing the performance ofthe product.

To further explain bubbles 702-708, several examples are now given.According to embodiments, products represented by small green bubbles702 may have good performance (based on, for example, historical salesdata) and may be highly similar to a lot of other products in theassortment, i.e. not unique top performers. That is, small green bubbles702 may represent well-selling products that have many substitutableproducts. According to embodiments, when some products represented bysmall green bubbles 702 are removed from the assortment, global saleswill remain largely unchanged, but may slightly decrease based, at leastin part, on one or more good performers being removed from theassortment.

In addition, products represented by big green bubbles 704 may have goodperformance and may be highly unique in the assortment, i.e. unique topperformers. That is, big green bubbles 704 may represent well-sellingproducts, which are unique. When top performers are too unique, theremay be a larger risk for that product to satisfy all the demand for theattribute values of that product. According to embodiments, whenadditional products with similar attribute values to the productsrepresented by big green bubbles 704 are added to the assortment,overall sales may increase, because customers have different options fora set of attribute values associated with these products.

Further, products represented by small red bubbles 706 may have badperformance and may be highly similar to other products in theassortment, i.e. not unique bad performers. That is, small red bubbles706 may represent products that are not performing well and are notunique. According to embodiments, when products represented by small redbubbles 706 are removed from the assortment, sales may not be impactedmuch because, in this case, every customer in the store would likelyfind substitutable products.

Finally, products represented by big red bubbles 708 may have badperformance, and few similar products in the assortment, i.e. unique badperformers. That is, big red bubbles 708 may represent products that arenot performing well, but which are highly unique. According toembodiments, removing products represented by big red bubbles 708 fromthe assortment may impact the global sales since only a fewsubstitutable products exist for these products, which in turn, meansthat sales to customers, will likely be lost.

Although particular representations of bubbles 702-708 are shown anddescribed as particular colors and sizes, embodiments contemplate anyparticular size or color, representing any particular characteristics,according to particular needs. For example, according to someembodiments, bubbles may comprise a medium-sized bubble that indicatesan amount of substitutability that is within a preselected ormedium-amount of substitutability.

FIG. 8 illustrates an exemplary view of interactive two-dimensionalvisualization 700 of FIG. 7 , according to an embodiment. Visualizationmodule 202 of assortment planner 110 provides a more in-depth analysisby providing a visualization of transferable demand links 802 associatedwith products in an assortment. For example, embodiments contemplate anability to look at the planning problem in different ways by navigatingthrough transferable demand links 802 to identify similar products foreach product as well as overlaying information in overlay 804. Overlay804 may display information, such as, for example, the amount ofexclusive sales that a product has as well as its historical salesperformance.

The combination of different insights on a product's performance anduniqueness, provides visibility in making insightful decisions on whichproducts to keep or remove from a product assortment. That is,interactive two-dimensional visualization 700 identifies products whichare similar in the way they should be dealt with when choosing if theyshould be kept or removed. According to embodiments, interactivetwo-dimensional visualization 700 provides an interface to add or removeproducts from a product assortment.

FIG. 9 illustrates an exemplary method 900 of generating a productassortment using interactive two-dimensional visualization 700,according to an embodiment. Although actions of the method are describedin a particular order, embodiments contemplate actions performed in anysuitable order or combination according to particular needs.

At action 902, visualization module 204 displays icons representing twoor more products in an assortment on interactive two-dimensionalvisualization 700, where the location of the icons indicates thesimilarity of the products represented by the icons. Although theexamples are illustrated with icons comprising bubbles 702-708,embodiments contemplate any suitable icon according to particular needs.As explained above, products from an assortment are graphed onto theinteractive two-dimensional visualization 700 according to similarity toprovide an interface to select, add, and remove products from theproduct assortment, where products that are closer together are moresimilar, and products further away are less similar.

At action 904, visualization module 204 modifies icons to indicatesecondary information, such as, for example, sales, uniqueness, and thelike. For example, as described above, a size of the projected bubbles502 may graphically indicate uniqueness, and a color of bubbles 502 maybe used to indicate past sales performance. Although size and color areused to indicate uniqueness and past sales performance, embodimentscontemplate any modification of icons to represent additionalinformation according to particular needs.

At action 906, visualization module 204 identifies user selection of anicon, and, in response to the user selection, automatically generatestransferable demand links 802 connecting the selected icon and all iconsrepresenting products which are similar to the product represented bythe selected icon. For example, transferable demand indicates thepredicted change in sales based on adding or removing an item from theproduct assortment. According to embodiments, transferable demand linksrepresent transferable demand that meet a preselected threshold, suchas, for example, greater than 40%, 50%, or any other preselected value.

At action 908, based on the connections between the selected product andall similar products, a user may choose to add a new product to theproduct assortment or remove a current product from the productassortment. For example, according to embodiments, assortment planner110 may remove from the assortment one or more products represented bysmall red bubbles which indicate products that do not bring additionalsales to the assortment. Further, assortment planner 110 may addproducts similar to the products represented by big green bubbles todiminish the exclusivity of these products (and which may result invisualization module 204 automatically recalculating and reducing thesize of these bubbles). Additionally, assortment planner 110 removeproducts which have transferable demand links to one or more keyproducts to reduce or eliminate substitutable options. According toembodiments, assortment planner 110 may test different assortments,including automatically testing different assortments. By testingdifferent to find a mix of products that will generate increased sales.

At action 910, visualization module 204 identifies the modified productassortment and, in response, recalculates transferable demand. Accordingto embodiments, visualization module 204 updates transferable demandlinks and bubble size and placement automatically based on the detectionof a product being added or removed from the assortment or themodification of one or more attribute values of one or more products.

Although particular examples are given, embodiments contemplate removalor addition of any retail product from a product assortment, accordingto particular needs.

At action 912, assortment purchase planner 210 initiates one or moresupply chain processes, as described above, to alter ranging decisionsand inventory at one or more supply chain entities in reflect the newproduct assortment.

To further illustrate, addition and removal of an item from anassortment, an example is now given.

FIG. 10 illustrates interactive two-dimensional visualization 700,according to a further embodiment. As discussed above, visualizationmodule 204 generates bubbles 702-708 representing products in a productassortment where small green bubbles 702 represent not unique topperformers, big green bubbles 704 represent unique top performers, smallred bubbles 706 represent not unique bad performers, and large redbubbles 708 represent unique bad performers.

When visualization module 204 identifies user selection of an iconrepresenting a product, visualization module 204 automatically generatestransferable demand links 802 that identify those products which havetransferable demand above a preselected value. Based on the displayedbubbles 702-708, transferable demand links 802, and/or overlay 804, auser may choose to add or remove a product from the product assortment.For example, as discussed above, small red bubble 706 representsproducts that are not performing well and are not unique. These productsordinarily may be removed from an assortment without likely affectingglobal sales. As shown below, attribute values of products linked to theexemplary product represented by small red bubble 706 share similarattribute values and, therefore, removing this product should not affectfuture sales.

Assuming that a user removes the product represented by the small redbubble 706 (here, a shoe), visualization module 204 identifies themodified product assortment and, in response, recomputes the size of thebubbles for the one or more products in the assortment. For example, thesize of bubble 706 may grow larger when a substitutable product isremoved. However, in response to the removal of the substitutableproduct, assortment planner 110 may remove one or more additionalremovals of products from the assortment (or additions of differentproducts to the assortment) to further modify the assortment. Thesemodifications may be based, at least in part, on the changing size andcolors of the one or more bubbles. In response to a finalized productassortment, assortment purchase planner 210 initiates a supply chainprocess, as described above, to alter inventory at one or more supplychain entities in reflect the new product assortment.

Additionally, or in the alterative, embodiments of interactivetwo-dimensional visualization 700 having transferable demand links 802provides an interface to change the values of one or more attributesassociated with a product in the assortment. In response to a changedvalue in the interface, such as, for example, in response to a userassigning a new value to one or more attributes, the visualizationmodule automatically displays the recalculated values. For example, if achange is made to the importance of the attributes, such as, brandimportance, the visualization module automatically recalculates theimpact on the similarity of the products in the assortment based, atleast in part on, and in response to the changed importance value. Asanother example, if a product is added or removed, the visualizationmodule recalculates and displays the effects of the product removal oraddition on the assortment.

Reference in the foregoing specification to “one embodiment”, “anembodiment”, or “some embodiments” means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the invention. The appearancesof the phrase “in one embodiment” in various places in the specificationare not necessarily all referring to the same embodiment.

While the exemplary embodiments have been shown and described, it willbe understood that various changes and modifications to the foregoingembodiments may become apparent to those skilled in the art withoutdeparting from the spirit and scope of the present invention.

What is claimed is:
 1. A computer-implemented method for planning aproduct assortment using an interactive visualization, comprising:receiving, by the computer, product attribute values for two or moreitems of a product assortment in a supply chain network comprising oneor more supply chain entities, wherein an inventory of the one or moresupply chain entities is used to store at least one of the one or moreitems; displaying, by the computer, two or more icons on an interactivevisualization of a graphical user interface via computer display,wherein each of the two or more icons is associated with each item ofthe two or more items, the distance between each of the two or moreicons indicating the similarity of the two or more items; in response toa user selection of an icon of the two or more icons, connecting, by thecomputer, the icon of the two or more icons with one or more other iconsof the two or more icons with transferable demand links, thetransferable demand links identifying transferable demand; identifying,by the computer, an item of the two or more items to be removed from theproduct assortment; removing, by the computer, the item of the two ormore items from the product assortment; and based, at least in part, ontransferable demand links associated with the removed item, causingrobotic machinery, by the computer, to transport items among the one ormore supply chain entities to restock the inventory according to thecurrent state of items in the supply chain network.
 2. Thecomputer-implemented method of claim 1, wherein each icon of the two ormore icons graphically indicate uniqueness and past sales performance ofthe product associated with each icon.
 3. The computer-implementedmethod of claim 2, further comprising: in response to removing the itemof the two or more items from the product assortment, recalculatingtransferable demand between at least two icons displayed on theinteractive visualization.
 4. The computer-implemented method of claim3, wherein the two or more icons indicate one or more of not unique topperformers, unique top performers, not unique bad performers, and uniquebad performers.
 5. The computer-implemented method of claim 4, whereinthe interactive visualization comprises an interface providing formodification of at least one of the product attribute values for the twoor more items in the supply chain network.
 6. The computer-implementedmethod of claim 5, further comprising: monitoring the attribute valuesof the two or more items; and in response to detection of a modifiedvalue of the product attribute values, automatically recalculatingsimilarity between at least two items in the product assortment.
 7. Thecomputer-implemented method of claim 6, wherein each icon comprises abubble, wherein a size of the bubble indicates uniqueness and a color ofthe bubble indicates past sales performance.
 8. A system of aninteractive transferable demand visualization, comprising: a computercomprising a computer display, a processor and a memory and configuredto: receive product attribute values for two or more items of a productassortment in a supply chain network comprising one or more supply chainentities, wherein an inventory of the one or more supply chain entitiesis used to store at least one of the one or more items; display of thecomputer display, on an interactive visualization graphical userinterface, two or more icons wherein each of the two or more icons isassociated with each item of the two or more items, the distance betweeneach of the two or more icons indicating the similarity of the two ormore items; in response to a user selection of an icon of the two ormore icons, connect the icon of the two or more icons with one or moreother icons of the two or more icons with transferable demand links, thetransferable demand links identifying transferable demand; identify anitem of the two or more items to be removed from the product assortment;and remove the item of the two or more items from the productassortment; and robotic machinery that, based at least in part, ontransferable demand links associated with the removed item, transportitems among the one or more supply chain entities to restock theinventory according to the current state of items in the supply chainnetwork.
 9. The system of claim 8, wherein each icon of the two or moreicons graphically indicates uniqueness and past sales performance of theproduct associated with each icon.
 10. The system of claim 9, whereinthe computer is further configured to: in response to removing the itemof the two or more items from the product assortment, recalculatetransferable demand between at least two icons displayed on theinteractive visualization.
 11. The system of claim 10, wherein the twoor more icons indicate one or more of not unique top performers, uniquetop performers, not unique bad performers, and unique bad performers.12. The system of claim 11, wherein the interactive visualizationcomprises an interface providing for modification of at least one of theproduct attribute values for the two or more items in the supply chainnetwork.
 13. The system of claim 12, wherein the computer is furtherconfigured to: monitor the attribute values of the two or more items;and in response to detection of a modified value of the productattribute values, automatically recalculate similarity between at leasttwo items in the product assortment.
 14. The system of claim 13, whereineach icon comprises a bubble, wherein a size of the bubble indicatesuniqueness and a color of the bubble indicates past sales performance.15. A non-transitory computer-readable medium embodied with software,the software when executed by a computer provides interactive productassortment planning and visualization by causing the computer to performthe following steps: receiving product attribute values for two or moreitems of a product assortment in a supply chain network comprising oneor more supply chain entities, wherein an inventory of the one or moresupply chain entities is used to store at least one of the one or moreitems; displaying two or more icons on an interactive visualizationgraphical user interface via a computer display, wherein each of the twoor more icons is associated with each item of the two or more items, thedistance between each of the two or more icons indicating the similarityof the two or more items; in response to a user selection of an icon ofthe two or more icons, connecting the icon of the two or more icons withone or more other icons of the two or more icons with transferabledemand links, the transferable demand links identifying transferabledemand; identifying an item of the two or more items to be removed fromthe product assortment; removing the item of the two or more items fromthe product assortment; and based, at least in part, on transferabledemand links associated with the removed item, causing robotic machineryto transport items among the one or more supply chain entities torestock the inventory according to the current state of items in thesupply chain network.
 16. The non-transitory computer-readable medium ofclaim 15, wherein each icon of the two or more icons graphicallyindicate uniqueness and past sales performance of the product associatedwith each icon.
 17. The non-transitory computer-readable medium of claim16, wherein the software when executed is further configured to: inresponse to removing the item of the two or more items from the productassortment, recalculate transferable demand between at least two iconsdisplayed on the interactive visualization.
 18. The non-transitorycomputer-readable medium of claim 17, wherein the two or more iconsindicate one or more of not unique top performers, unique topperformers, not unique bad performers, and unique bad performers. 19.The non-transitory computer-readable medium of claim 18, wherein theinteractive visualization comprises an interface providing formodification of at least one of the product attribute values for the twoor more items in the supply chain network.
 20. The non-transitorycomputer-readable medium of claim 19, wherein the software when executedis further configured to: monitor the attribute values of the two ormore items; and in response to detection of a modified value of theproduct attribute values, automatically recalculate similarity betweenat least two items in the product assortment.